| Texto completo | |
| Autor(es): |
Silva, Jonathan de Andrade
;
Hruschka, Eduardo Raul
;
Gama, Joao
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | EXPERT SYSTEMS WITH APPLICATIONS; v. 67, p. 228-238, JAN 2017. |
| Citações Web of Science: | 25 |
| Resumo | |
Several algorithms for clustering data streams based on k-Means have been proposed in the literature. However, most of them assume that the number of clusters, k, is known a priori by the user and can be kept fixed throughout the data analySis process. Besides the difficulty in choosing k, data stream clustering imposes several challenges to be addressed, such as addressing non-stationary, unbounded data that arrive in an online fashion. In this paper, we propose a Fast Evolutionary Algorithm for Clustering data streams (FEAC-Stream) that allows estimating k automatically from data in an online fashion. FEAC-Stream uses the Page-Hinkley Test to detect eventual degradation in the quality of the induced clusters, thereby triggering an evolutionary algorithm that re-estimates k accordingly. FEAC-Stream relies on the assumption that clusters of (partially unknown) data can provide useful information about the dynamics of the data stream. We illustrate the potential of FEAC-Stream in a set of experiments using both synthetic and real-world data streams, comparing it to four related algorithms, namely: CluStream-OMRk, CluStream-BkM, StreamKM++-OMRk and StreamKM++-BkM. The obtained results show that FEAC-Stream provides good data partitions and that it can detect, and accordingly react to, data changes. (C) 2016 Elsevier Ltd. All rights reserved. (AU) | |
| Processo FAPESP: | 10/15049-7 - Agrupamento de dados em Fluxos Contínuos com Estimativa Automática do Número de Grupos |
| Beneficiário: | Jonathan de Andrade Silva |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |